Drip Fertigation Technology for Enhancing Date Palm Productivity and Fruit Quality
Bibliographic record
Abstract
A field experiment was conducted during two successive growing seasons, 2014/2015 and 2015/2016 to evaluate the effect of different fertilizer application methods on date palm (Phoenix dactylifera L.) yield and fruit quality grown in sandy soil. Strip block statistical design with three replicates was used on four selected date palm cultivars (Madjool, Sacchari, Kheyarah and Sggaa) as main treatments and three fertilizer application methods (Hydraulic injector, Surface Broadcast and By-pass tank) as sub main treatments. The results revealed significant increases in yield and fruit quality when using continuous fertigation by Hydraulic injector comparing with broadcast and By-pass tank traditional methods. The average of two seasons results indicated also that using Hydraulic injector method maximized Sacchari date yield by producing 69 kg per tree and resulted in the best water productivity (1.06 kg m-3). The use of fertigation method has significantly increased the date palm productivity by 81, 51.2, 66.7 and 72.8% in comparison to the traditional Surface Broadcast method for Madjool, Sacchari, Kheyarah and Sggaa, respectively. The mean fruit weights were significantly increased by 56.5, 72.1, 90.2 and 68.8% when using the hydraulic injector compared to the traditional broadcast application method for pervious date palm cultivars, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".